scholarly journals Latency Aware and Service Delay with Task Scheduling in Mobile Edge Computing

2018 ◽  
Vol 10 (04) ◽  
pp. 127-141 ◽  
Author(s):  
Dileep Kumar Sajnani ◽  
Abdul Rasheed Mahesar ◽  
Abdullah Lakhan ◽  
Irfan Ali Jamali
Keyword(s):  
Task Scheduling ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Service Delay

scholarly journals An Overhead-Optimizing Task Scheduling Strategy for Ad-hoc Based Mobile Edge Computing

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 5609-5622 ◽  
Author(s):  
Li Tianze ◽  
Wu Muqing ◽  
Zhao Min ◽  
Liao Wenxing
Keyword(s):  
Task Scheduling ◽  
Ad Hoc ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Scheduling Strategy

scholarly journals Task Scheduling in Deadline-Aware Mobile Edge Computing Systems

2019 ◽  
Vol 6 (3) ◽  
pp. 4854-4866 ◽  
Author(s):  
Tongxin Zhu ◽  
Tuo Shi ◽  
Jianzhong Li ◽  
Zhipeng Cai ◽  
Xun Zhou
Keyword(s):  
Task Scheduling ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computing Systems

Joint Task Scheduling and Energy Management for Heterogeneous Mobile Edge Computing With Hybrid Energy Supply

2020 ◽  
Vol 7 (9) ◽  
pp. 8419-8429 ◽  
Author(s):  
Ying Chen ◽  
Yongchao Zhang ◽  
Yuan Wu ◽  
Lianyong Qi ◽  
Xin Chen ◽  
...  
Keyword(s):  
Energy Management ◽  
Task Scheduling ◽  
Energy Supply ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Hybrid Energy ◽  
Joint Task

scholarly journals Computation Offloading Strategy in Mobile Edge Computing

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 191 ◽  
Author(s):  
Jinfang Sheng ◽  
Jie Hu ◽  
Xiaoyu Teng ◽  
Bin Wang ◽  
Xiaoxia Pan
Keyword(s):  
Decision Making ◽  
User Experience ◽  
Task Scheduling ◽  
Mobile Internet ◽  
Edge Computing ◽  
Computation Offloading ◽  
Smart Devices ◽  
Mobile Edge Computing ◽  
Network Bandwidth ◽  
Intelligent Devices

Mobile phone applications have been rapidly growing and emerging with the Internet of Things (IoT) applications in augmented reality, virtual reality, and ultra-clear video due to the development of mobile Internet services in the last three decades. These applications demand intensive computing to support data analysis, real-time video processing, and decision-making for optimizing the user experience. Mobile smart devices play a significant role in our daily life, and such an upward trend is continuous. Nevertheless, these devices suffer from limited resources such as CPU, memory, and energy. Computation offloading is a promising technique that can promote the lifetime and performance of smart devices by offloading local computation tasks to edge servers. In light of this situation, the strategy of computation offloading has been adopted to solve this problem. In this paper, we propose a computation offloading strategy under a scenario of multi-user and multi-mobile edge servers that considers the performance of intelligent devices and server resources. The strategy contains three main stages. In the offloading decision-making stage, the basis of offloading decision-making is put forward by considering the factors of computing task size, computing requirement, computing capacity of server, and network bandwidth. In the server selection stage, the candidate servers are evaluated comprehensively by multi-objective decision-making, and the appropriate servers are selected for the computation offloading. In the task scheduling stage, a task scheduling model based on the improved auction algorithm has been proposed by considering the time requirement of the computing tasks and the computing performance of the mobile edge computing server. Extensive simulations have demonstrated that the proposed computation offloading strategy could effectively reduce service delay and the energy consumption of intelligent devices, and improve user experience.

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